slowave 0.1.8

Brain-inspired long-term memory for AI agents — zero LLM during ingest or retrieval

Slowave

Brain-inspired, latent-space long-term memory for MCP-compatible AI agents, coding assistants, and chats.
Shared across sessions, clients, and tools.

Slowave plugs into Claude Code, Cline, and Claude Desktop through MCP, giving them a shared local memory that accumulates, consolidates, adapts, and recalls across sessions.

Slowave is not just a transcript store or a conventional RAG layer. It is inspired by the idea that memory is a dynamic system shaped by association, time, salience, replay, and retrieval. The default memory path uses local embeddings, SQLite, FAISS, deterministic geometry, and background consolidation — without requiring an LLM call in the core memory loop.

Slowave core idea: sessions create episodes, replay distills recurring patterns, time changes salience, contradiction-aware updates keep memory current, and recall reinforces memories that prove useful.

Status

v0.1.6 — Alpha. The core local memory path, MCP server, CLI, and dashboard are functional. Python 3.10–3.12 is supported and tested; Python 3.13 is not yet supported.

Run slowave doctor after installing to verify your environment.

At a glance

What you get	Why it matters
No LLM in the core memory loop	No API key, cloud extraction step, or per-query model call required. Memory ingest, consolidation, and retrieval are all LLM-free.
Privacy-first local memory	Memory is stored and processed locally; no cloud memory backend, API extraction step, or remote LLM inference is required.
Local CPU inference	Uses BAAI/bge-small-en-v1.5 embeddings, SQLite, FAISS, and deterministic geometry.
Brain-inspired consolidation	Raw events become episodes; episodes replay into prototypes; prototypes consolidate into latent schemas.
Active recall	Retrieved memories are reinforced, so recall changes the memory system over time.
Time-aware memory	Salience, decay, temporal anchors, supersession, and contradiction-aware updates help keep memory current. Episodes are date-stamped (ISO 8601) and recalled with temporal context.
Gated working memory	slowave_context injects a compact, cue-relevant brief instead of dumping history into the prompt.

flowchart LR
    A([💬 Events]) -->|session end| B([🧠 Episodes])
    B -->|replay| C([🌀 Prototypes])
    C -->|consolidation| D([📖 Schemas])
    D -->|recall| E([⚡ Context brief])
    E -.->|reinforces| B
    E -.->|reinforces| C

    style A fill:#2d4a3e,stroke:#4caf87,color:#e8f5e9
    style B fill:#1a3a5c,stroke:#4a9eff,color:#e3f0ff
    style C fill:#3a2d5c,stroke:#9b7fee,color:#f0ebff
    style D fill:#4a2d1a,stroke:#ff9944,color:#fff3e0
    style E fill:#1a3a2d,stroke:#44cc88,color:#e8f5e9

How it works

A typical Slowave flow looks like this:

1. You work with Cline, Claude Code, or Claude Desktop on a project or conversation.
2. The client writes task-relevant observations through MCP.
3. When the session ends, Slowave forms an episode.
4. The background worker consolidates accumulated episodes into prototypes and schemas.
5. In a future session, slowave_context retrieves a compact, relevant memory brief.
6. If a memory is useful during recall, it is reinforced.

Slowave represents memory primarily through embeddings, metadata, salience, temporal structure, and learned geometry rather than repeated LLM summarization.

What Slowave is for

Slowave is a local long-term memory substrate for tools that otherwise forget between sessions. It can remember:

Memory type	Examples
Project context	Conventions, architectural decisions, recurring commands
Personal context	Preferences, communication style, planning patterns
Work history	Debugging sessions, lessons learned, open questions
Constraints	Warnings, artifacts, superseded decisions, active assumptions
Desktop chat context	Non-coding conversations that should persist locally

Existing memory systems often retrieve stored text or ask an LLM to rewrite memories. Slowave treats memory as a living system: sessions create episodes, replay distills patterns, time changes salience, contradiction-aware updates revise beliefs, and recall reinforces what was useful.

Why Slowave is different

Most agent memory systems fall into one of two categories:

1. Transcript stores that save and retrieve previous messages.
2. LLM-written memory systems that ask a model to extract, rewrite, summarize, or update memories.

Slowave takes a different path. It keeps the default memory loop local, embedding-based, and geometry-driven. Memories are not only retrieved; they evolve through salience, decay, replay, activation, and consolidation.

This makes Slowave useful when you want:

Need	Slowave approach
Shared memory across tools	One local store can be used by Claude Code, Cline, Claude Desktop, and other MCP clients.
Low-friction local setup	No hosted memory service or remote inference backend is required.
Compact context injection	slowave_context returns a short, cue-relevant memory brief.
Memory that changes over time	Recall, decay, salience, and replay affect future retrieval.
Project continuity	Conventions, decisions, constraints, and lessons learned can survive across sessions.
Inspectability	The CLI and dashboard help inspect the local memory state.

Install in minutes

Installing Slowave gives you two commands:

Command	Purpose
slowave	CLI, dashboard, manual recall, debugging, and research workflows.
slowave-mcp	MCP server used by Claude Desktop, Claude Code, and Cline.

Choose one install path:

# Recommended — isolated, no virtualenv management needed
pipx install slowave

# pip
pip install slowave

# macOS Homebrew (formula lives in the main repo)
brew tap mrsalty/slowave https://github.com/mrsalty/slowave
brew install slowave

Install from source:

git clone https://github.com/mrsalty/slowave
cd slowave
python3 -m venv .venv
source .venv/bin/activate
pip install -e .

Verify your environment:

slowave doctor           # check Python version, torch, faiss, embedding backend
slowave --help
slowave-mcp --help
slowave stats

Default storage is:

~/.slowave/slowave.db

No Ollama, OpenRouter, hosted vector database, cloud memory service, or other LLM backend is required for the default local path.

Connect a client

Client	Setup guide
Claude Desktop	integrations/claude-desktop/
Claude Code	integrations/claude-code/
Cline	integrations/cline/

[!IMPORTANT] MCP setup alone is not enough. Each client needs the MCP configuration, instruction/rules injection, and the background worker.

Setup layer	Why it matters
MCP configuration	Makes the slowave_* tools visible to the client.
Instruction/rules injection	Makes the client actually call Slowave during the task.
Background worker	Consolidates episodes into durable schemas for better future context.

Episodes are created immediately when a session ends. The worker turns accumulated episodes into durable latent schemas for better future slowave_context injection.

Local dashboard

Run a read-only web UI for memory inspection:

slowave dashboard
# open http://127.0.0.1:8765

The dashboard binds to 127.0.0.1 by default and shows DB health, Slowave/MCP processes, schemas, a recall playground, and a schema graph.

CLI usage

The CLI is useful for debugging, manual memory writes, dashboard access, and benchmark/research workflows. It should not be the first path for most users; real agent memory needs MCP plus prompt/rules injection.

See docs/cli.md for the command list and a CLI-only quickstart.

Documentation

Guide	Covers
integrations/	Fast client-specific setup guides for Claude Desktop, Claude Code, and Cline
docs/architecture.md	Brain-inspired mechanisms, data flow, storage, recall, consolidation
docs/design.md	Why the LLM path was removed from the memory loop
docs/dashboard.md	Local dashboard guide
docs/cli.md	CLI quickstart and command reference
docs/benchmarks.md	Benchmark results, run conditions, per-category breakdown
docs/limitations.md	Known limitations: schema quality, language support, scale
docs/reproducibility.md	How to reproduce benchmark numbers

Benchmarks

Alpha-stage numbers. Internal runs, not independently verified. Treat as directional. See docs/benchmarks.md for full run conditions and known gaps.

All runs: brain-only path, local CPU, BAAI/bge-small-en-v1.5 embeddings, SQLite + FAISS, zero LLM calls.

Two modes are reported. The with-consolidation numbers (70.0% / 74.6%) represent the full Slowave pipeline: sessions → episodes → replay → latent schemas → recall. The episode-only baseline (60.2% / 74.6%) is retrieval without consolidation — episodes recalled directly, no schemas. The difference shows the contribution of the consolidation layer.

Overall results

Benchmark	Questions	With consolidation	Episode-only baseline	Cosine-only ablation¹
LongMemEval	500	70.0%	60.2%	~60.0%
LoCoMo	1 986	74.6%	74.6%²	~68.0%

Metric: keyword hit-rate. All runs: zero LLM calls, ~10 ms recall latency, data on device.

¹ Cosine-only ablation: spreading activation, graph expansion, and transition model all disabled.
² LoCoMo is multi-session by design; episode retrieval already captures most of the signal and consolidation adds schemas on top of a strong baseline.

Deep Memory Retrieval (DMR)

DMR (MemGPT paper) tests factual recall across multi-session persona conversations: 10 personas × 10 questions = 100 questions total. Published baselines from arXiv:2501.13956.

System	Score	LLM calls	Cost
Slowave v0.1.5	95.0%	0	$0.00
Zep (SOTA)	94.8%	Many	$
MemGPT baseline	93.4%	Many	$

Slowave beats both published LLM-augmented baselines with zero API cost and ~9 ms recall latency.

LongMemEval per-category (with consolidation)

Category	Score	Notes
Single-session-user	91.4%	✅ strong
Knowledge-update	92.3%	✅ strong
Single-session-assistant	66.1%	✅ solid
Temporal-reasoning	67.7%	✅ solid
Multi-session	60.9%	⚠ number aggregation gap
Single-session-preference	20.0%	⚠ preference abstraction gap

LoCoMo per-category (with consolidation)

Category	Score	Notes
Multi-session	86.2%	✅ strong cross-session recall
Adversarial	82.3%	✅ robust
Single-session	64.9%	✅ solid
Temporal	56.1%	✅ solid
Commonsense	27.1%	— world knowledge not in store

Known gaps

Gap	Root cause	Status
Temporal date arithmetic	"How many days between X and Y?" requires arithmetic, not retrieval	Open — answer-construction layer
Multi-session aggregation (LME 60.9%)	Summing quantities across episodes — no single episode holds the answer	Open — explicit aggregation layer
Preference abstraction (LME 20%)	Implicit preferences not abstracted into queryable schema entries	Open — preference-extraction layer

Language support

All core memory operations are language-agnostic — episode storage, embedding, retrieval, FAISS search, salience, spreading activation, the prototype graph, and multi-scale consolidation work on embedding vectors and numeric metadata with no language dependency.

Two components are English-only:

Component	English-only reason	Fallback for non-English
Temporal anchor probe (Stage 10)	Pre-embedded English landmark phrases ("last month", "two weeks ago") calibrate the temporal compass	Temporal re-ranking defaults to "now" — correct for atemporal queries, slightly suboptimal for past-anchored ones
VSA dep-parse mode (vsa_mode="ner")	Uses spaCy en_core_web_sm dependency parser for subject/predicate/object role extraction	Use vsa_mode="geometric" (default, language-agnostic) or vsa_mode="lexical" (regex-based, no model dependency)

The temporal probe phrase list is in slowave/latent/temporal.py (_TEMPORAL_PROBES). Adding phrases in other languages extends the compass to those languages without any other code change.

For a full language support matrix and multi-language deployment guide, see docs/limitations.md.

For full per-category results, run conditions, and known gaps see docs/benchmarks.md.
For evaluation scripts and reproduction steps see docs/reproducibility.md.
For known limitations see docs/limitations.md.

License

Slowave is licensed under the GNU Affero General Public License v3.0 or later starting with version 0.1.5.

Versions published before 0.1.5 were released under the MIT License; those earlier releases remain available under the terms they were originally published with.

This license keeps Slowave open for research, experimentation, and community use while ensuring that modified versions offered over a network make their source available under the same terms. Commercial licenses may be available for organizations that want to use Slowave in proprietary products, hosted services, or other contexts where AGPL compliance is not suitable. See COMMERCIAL.md.